Augmented Photos integrating Environmental Information

small idea. Pictures or video are supports to remember, reconstruct our experiences of life and a way to transfert them without spatio-temoral constraints. In regards of the current green trend, can I also add environemtal information to such support of memory to capture also environmental dimensions of my experience? Can I take augmented pictures capturing also environmental elements, so people watching the picture could perceive for instance my exposure to pollution at that time?

Pollution-tagged picture: proof of concept

Geotagging [1] was introduced in gps-built-in smartphones. Such feature injects geographical information (lat, lng) in the metadata header (EXIF header) [2] of the picture. Simple but powerful:  a new dimension of the user experience is captured and added to the picture on top of the visual one. Such type of information has been recently (2002) added in the specification of theEXIF format and is now recognized by web services like Flickr, able to map such pictures.

Like Geotagging, Environmental tagging will inject environmental information in the picture, for instance adding the level of exposure to noise in dB(A) of the user in the picture – other kind of sensor-related information e.g. physiological info. is also possible. How to do that? The process is simple using a home-made mobile program. When I take a picture with this program, I also record the noise level (every 125ms/1s) via the microphone and a real-time algorithm computing the noise level. Then I write the temporal serie of noise levels in an EXIF property of the taken picture (e.g. “noise:12,35,32,40,23″). The program can even add a semantic tag representing the average level according to different classes e.g. “noisepollution:quite”, “annoying”, “noisy”.

Figure 1. Automatic pollution-tagging of pictures

Of course adding environmental information in a picture is a hack and is not recognized by any web service. But Let’s imagine it is. Then I upload the picture to Flickr (What I did actually after having added fake environmental info. to a given picture). At the end every reader could see what was the level of pollution that was exposed the photograph at this location thanks to the tag “noisepollution:veryhigh” (Figure 2 A). Furthermore computer could use the raw measurements stored in the EXIF header to compute further analysis.

Semantic tag
The tag “noisepollution:veryhigh” allows the reader to know the level of exposure of the user while taking the picture, extending the notion of « snapshot » by capturing an new environmental dimension of the user’s experience.
Exif Data 
The EXIF header with the “noise” property storing the raw measurements, allowing further analysis

Check the flickr image

Reconstructing a map of the collective exposure to pollution

At a collective level, by collecting all the pollution tagged photos, we could be able to create a memory of the population exposure. A map of the pollution level experienced by the all users might be reconstructed via with pictures.

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